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# Fitting - PowerPoint PPT Presentation

Fitting. Fitting. We’ve learned how to detect edges, corners, blobs. Now what? We would like to form a higher-level, more compact representation of the features in the image by grouping multiple features according to a simple model. 9300 Harris Corners Pkwy, Charlotte, NC. Fitting.

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Presentation Transcript

• We’ve learned how to detect edges, corners, blobs. Now what?

• We would like to form a higher-level, more compact representation of the features in the image by grouping multiple features according to a simple model

9300 Harris Corners Pkwy, Charlotte, NC

• Choose a parametric model to represent a set of features

simple model: circles

simple model: lines

complicated model: car

Source: K. Grauman

• Noise in the measured feature locations

• Extraneous data: clutter (outliers), multiple lines

• Missing data: occlusions

Case study: Line detection

Fitting: Overview

• If we know which points belong to the line, how do we find the “optimal” line parameters?

• Least squares

• What if there are outliers?

• Robust fitting, RANSAC

• What if there are many lines?

• Voting methods: RANSAC, Hough transform

• What if we’re not even sure it’s a line?

• Model selection

• Data: (x1, y1), …, (xn, yn)

• Line equation: yi = mxi + b

• Find (m, b) to minimize

y=mx+b

(xi, yi)

Normal equations: least squares solution to XB=Y

• Not rotation-invariant

• Fails completely for vertical lines

• Distance between point (xi, yi) and lineax+by=d (a2+b2=1): |axi + byi – d|

ax+by=d

Unit normal: N=(a, b)

(xi, yi)

• Distance between point (xi, yi) and lineax+by=d (a2+b2=1): |axi + byi – d|

• Find (a, b, d) to minimize the sum of squared perpendicular distances

ax+by=d

Unit normal: N=(a, b)

(xi, yi)

• Distance between point (xi, yi) and lineax+by=d (a2+b2=1): |axi + byi – d|

• Find (a, b, d) to minimize the sum of squared perpendicular distances

ax+by=d

Unit normal: N=(a, b)

(xi, yi)

Solution to (UTU)N = 0, subject to ||N||2 = 1: eigenvector of UTUassociated with the smallest eigenvalue (least squares solution to homogeneous linear systemUN = 0)

second moment matrix

second moment matrix

N = (a, b)

• Generative model: line points are sampled independently and corrupted by Gaussian noise in the direction perpendicular to the line

ax+by=d

(u, v)

ε

(x, y)

point on the line

normaldirection

noise:sampled from zero-meanGaussian withstd. dev. σ

Least squares as likelihood maximization

• Generative model: line points are sampled independently and corrupted by Gaussian noise in the direction perpendicular to the line

ax+by=d

(u, v)

ε

(x, y)

Likelihood of points given line parameters (a, b, d):

• Least squares fit to the red points:

• Least squares fit with an outlier:

Problem: squared error heavily penalizes outliers

• General approach: find model parameters θ that minimizeri(xi, θ) – residual of ith point w.r.t. model parametersθρ – robust function with scale parameter σ

The robust function ρ behaves like squared distance for small values of the residual u but saturates for larger values of u

The effect of the outlier is minimized

The error value is almost the same for everypoint and the fit is very poor

Behaves much the same as least squares

• Robust fitting is a nonlinear optimization problem that must be solved iteratively

• Least squares solution can be used for initialization

• Adaptive choice of scale: approx. 1.5 times median residual (F&P, Sec. 15.5.1)

• Robust fitting can deal with a few outliers – what if we have very many?

• Random sample consensus (RANSAC): Very general framework for model fitting in the presence of outliers

• Outline

• Choose a small subset of points uniformly at random

• Fit a model to that subset

• Find all remaining points that are “close” to the model and reject the rest as outliers

• Do this many times and choose the best model

M. A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM, Vol 24, pp 381-395, 1981.

Source: R. Raguram

Least-squares fit

Source: R. Raguram

• Randomly select minimal subset of points

Source: R. Raguram

• Randomly select minimal subset of points

• Hypothesize a model

Source: R. Raguram

• Randomly select minimal subset of points

• Hypothesize a model

• Compute error function

Source: R. Raguram

• Randomly select minimal subset of points

• Hypothesize a model

• Compute error function

• Select points consistent with model

Source: R. Raguram

• Randomly select minimal subset of points

• Hypothesize a model

• Compute error function

• Select points consistent with model

• Repeat hypothesize-and-verify loop

Source: R. Raguram

• Randomly select minimal subset of points

• Hypothesize a model

• Compute error function

• Select points consistent with model

• Repeat hypothesize-and-verify loop

Source: R. Raguram

Uncontaminated sample

• Randomly select minimal subset of points

• Hypothesize a model

• Compute error function

• Select points consistent with model

• Repeat hypothesize-and-verify loop

Source: R. Raguram

• Randomly select minimal subset of points

• Hypothesize a model

• Compute error function

• Select points consistent with model

• Repeat hypothesize-and-verify loop

Source: R. Raguram

• Repeat N times:

• Draw s points uniformly at random

• Fit line to these s points

• Find inliers to this line among the remaining points (i.e., points whose distance from the line is less than t)

• If there are d or more inliers, accept the line and refit using all inliers

• Initial number of points s

• Typically minimum number needed to fit the model

• Distance threshold t

• Choose t so probability for inlier is p (e.g. 0.95)

• Zero-mean Gaussian noise with std. dev. σ: t2=3.84σ2

• Number of samples N

• Choose N so that, with probability p, at least one random sample is free from outliers (e.g. p=0.99) (outlier ratio: e)

Source: M. Pollefeys

• Initial number of points s

• Typically minimum number needed to fit the model

• Distance threshold t

• Choose t so probability for inlier is p (e.g. 0.95)

• Zero-mean Gaussian noise with std. dev. σ: t2=3.84σ2

• Number of samples N

• Choose N so that, with probability p, at least one random sample is free from outliers (e.g. p=0.99) (outlier ratio: e)

Source: M. Pollefeys

• Initial number of points s

• Typically minimum number needed to fit the model

• Distance threshold t

• Choose t so probability for inlier is p (e.g. 0.95)

• Zero-mean Gaussian noise with std. dev. σ: t2=3.84σ2

• Number of samples N

• Choose N so that, with probability p, at least one random sample is free from outliers (e.g. p=0.99) (outlier ratio: e)

Source: M. Pollefeys

• Initial number of points s

• Typically minimum number needed to fit the model

• Distance threshold t

• Choose t so probability for inlier is p (e.g. 0.95)

• Zero-mean Gaussian noise with std. dev. σ: t2=3.84σ2

• Number of samples N

• Choose N so that, with probability p, at least one random sample is free from outliers (e.g. p=0.99) (outlier ratio: e)

• Consensus set size d

• Should match expected inlier ratio

Source: M. Pollefeys

• Inlier ratio e is often unknown a priori, so pick worst case, e.g. 50%, and adapt if more inliers are found, e.g. 80% would yield e=0.2

• N=∞, sample_count =0

• While N >sample_count

• Choose a sample and count the number of inliers

• Set e = 1 – (number of inliers)/(total number of points)

• Recompute N from e:

• Increment the sample_count by 1

Source: M. Pollefeys

• Pros

• Simple and general

• Applicable to many different problems

• Often works well in practice

• Cons

• Lots of parameters to tune

• Doesn’t work well for low inlier ratios (too many iterations, or can fail completely)

• Can’t always get a good initialization of the model based on the minimum number of samples